Self-organization of probabilistic PCA models

Authors:
Ezequiel López-Rubio;Juan Miguel Ortiz-de-Lazcano-Lobato;Domingo López-Rodríguez;María Del Carmen Vargas-González
Affiliations:
School of Computer Engineering, University of Málaga, Málaga, Spain;School of Computer Engineering, University of Málaga, Málaga, Spain;School of Computer Engineering, University of Málaga, Málaga, Spain;School of Computer Engineering, University of Málaga, Málaga, Spain
Venue:
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Year:
2007

Citing 4
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Probabilistic PCA self-organizing maps

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Abstract

We present a new neural model, which extends Kohonen's self-organizing map (SOM) by performing a Probabilistic Principal Components Analysis (PPCA) at each neuron. Several self-organizing maps have been proposed in the literature to capture the local principal subspaces, but our approach offers a probabilistic model at each neuron while it has linear complexity on the dimensionality of the input space. This allows to process very high dimensional data to obtain reliable estimations of the local probability densities which are based on the PPCA framework. Experimental results are presented, which show the map formation capabilities of the proposal with high dimensional data.